Time Until Large Events

import pandas as pd
import numpy as np
import datetime as dt

csv_file = "../datasets/Formatted_ETAS_Output.csv"
etas = pd.read_csv(csv_file, sep = ',', lineterminator='\n')
csv_file = "../datasets/All (1960-2023).csv"
usgs = pd.read_csv(csv_file, sep = ',', lineterminator='\n', dtype={'time':str})

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C:\Users\Vishal\AppData\Local\Temp\ipykernel_6948\2239509985.py:4: DtypeWarning: Columns (1,2,3,4,6,7,8,9,15,16,17,18) have mixed types. Specify dtype option on import or set low_memory=False.
  usgs = pd.read_csv(csv_file, sep = ',', lineterminator='\n', dtype={'time':str})

Data Filtering

1. Converting the date columns to datetime

2. Date > 1960-01-01 and < 2023-01-01

3. Longitude > -123 and < -113

4. Latitude > 29 and < 39

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etas["Date"] = pd.to_datetime(etas["Date"], errors="coerce", format="%m/%d/%y")
etas.loc[etas["Date"].dt.year > pd.Timestamp.now().year, "Date"] -= pd.DateOffset(years=100)
etas = etas[(etas['Date'] > pd.to_datetime('1960-01-01')) & (etas['Date'] < pd.to_datetime('2023-01-01'))]

#filter the dataset by X > -123 and X < -113 and Y > 29 and Y < 39
etas = etas[etas['X'] > -123]
etas = etas[etas['X'] < -113]
etas = etas[etas['Y'] < 39]
etas = etas[etas['Y'] > 29]

etas.head()

	Date	Time	Year	X	Y	Magnitude	Z\r
1	1960-01-02	0:08:49.00	1960.006125	-115.6222	33.0793	4.25	7.9322
2	1960-01-02	0:10:31.00	1960.007305	-115.6323	33.1220	3.03	8.4015
3	1960-01-02	0:10:32.00	1960.007320	-115.5851	33.0745	3.03	7.9678
4	1960-01-02	0:11:07.00	1960.007720	-115.6256	33.0290	3.08	7.9737
5	1960-01-02	0:11:17.00	1960.007840	-115.6050	33.0276	3.61	7.9322

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usgs["Date"] = pd.to_datetime(usgs["time"], errors="coerce").dt.strftime("%Y-%m-%d")
usgs.drop(columns=["time"], inplace=True)
usgs = usgs[(pd.to_datetime(usgs['Date']) > pd.to_datetime('1960-01-01')) & (pd.to_datetime(usgs['Date']) < pd.to_datetime('2023-01-01'))]

usgs['longitude'] = pd.to_numeric(usgs['longitude'], errors='coerce')
usgs['latitude'] = pd.to_numeric(usgs['latitude'], errors='coerce')
usgs['mag'] = pd.to_numeric(usgs['mag'], errors='coerce')

#filter the dataset by X > -123 and X < -113 and Y > 29 and Y < 39
usgs = usgs[usgs['longitude'] > -123]
usgs = usgs[usgs['longitude'] < -113]
usgs = usgs[usgs['latitude'] < 39]
usgs = usgs[usgs['latitude'] > 29]

time = []
for i in usgs['Date']:
    time.append(pd.to_datetime(i))
usgs['Date'] = time

usgs.head()

	latitude	longitude	depth	mag	magType	nst	gap	dmin	rms	net	...	place	type	horizontalError	depthError	magError	magNst	status	locationSource	magSource\r	Date
240	33.397500	-116.393333	3.88	4.14	mw	132	16	0.07391	0.19	ci	...	16 km N of Borrego Springs, CA	earthquake	0.1	0.38	NaN	6	reviewed	ci	ci\r	2022-12-31
241	34.355667	-116.921833	4.73	3.47	mw	121	25	0.07845	0.15	ci	...	11km SSE of Lucerne Valley, CA	earthquake	0.09	0.41	NaN	4	reviewed	ci	ci\r	2022-12-31
246	37.620167	-122.025000	3.82	3.34	mw	141	16	NaN	0.16	nc	...	3km N of Union City, CA	earthquake	0.1	0.17	NaN	3	reviewed	nc	nc\r	2022-12-22
262	37.918167	-122.304000	5.48	3.57	mw	170	19	0.01598	0.15	nc	...	1km ENE of El Cerrito, CA	earthquake	0.1	0.17	NaN	4	reviewed	nc	nc\r	2022-12-17
263	36.604667	-121.209333	8.88	3.28	ml	67	55	0.03812	0.09	nc	...	10km NW of Pinnacles, CA	earthquake	0.14	0.28	0.129	72	reviewed	nc	nc\r	2022-12-13

5 rows × 22 columns

Data Grouping And Merging

Data is grouped into 1 day chunks based on the max magnitude

max_mag_etas = pd.DataFrame(etas.groupby(etas['Date'].dt.to_period('D')).Magnitude.max())
max_mag_etas.reset_index(inplace=True)
max_mag_etas.head()

	Date	Magnitude
0	1960-01-02	4.25
1	1960-01-03	3.90
2	1960-01-04	4.24
3	1960-01-05	3.40
4	1960-01-06	3.47

max_mag_usgs = pd.DataFrame(usgs.groupby(usgs['Date'].dt.to_period('D')).mag.max())
max_mag_usgs.reset_index(inplace=True)
max_mag_usgs.head()

	Date	mag
0	1960-01-02	4.04
1	1960-01-05	3.03
2	1960-01-07	3.64
3	1960-01-08	3.10
4	1960-01-11	3.79

large_earthquake = 6

Large Events

A label is added to Large Event data

large_mag_etas = max_mag_etas.copy()
large_mag_etas["Large Event"] = (large_mag_etas["Magnitude"] > large_earthquake).astype(int)
large_mag_etas["Date"] = large_mag_etas["Date"].dt.to_timestamp()
large_mag_etas['time_diff'] = large_mag_etas.loc[large_mag_etas['Large Event'] == 1, 'Date'].diff().dt.days
large_mag_etas['time_diff'].iloc[0] = pd.NA

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C:\Users\Vishal\AppData\Local\Temp\ipykernel_6948\609931718.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  large_mag_etas['time_diff'].iloc[0] = pd.NA

large_mag_etas.head()

	Date	Magnitude	Large Event	time_diff
0	1960-01-02	4.25	0	NaN
1	1960-01-03	3.90	0	NaN
2	1960-01-04	4.24	0	NaN
3	1960-01-05	3.40	0	NaN
4	1960-01-06	3.47	0	NaN

large_mag_usgs = max_mag_usgs.copy()
large_mag_usgs["Large Event"] = (large_mag_usgs["mag"] > large_earthquake).astype(int)
large_mag_usgs["Date"] = large_mag_usgs["Date"].dt.to_timestamp()
large_mag_usgs['time_diff'] = large_mag_usgs.loc[large_mag_usgs['Large Event'] == 1, 'Date'].diff().dt.days
large_mag_usgs['time_diff'].iloc[0] = pd.NA

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C:\Users\Vishal\AppData\Local\Temp\ipykernel_6948\50271692.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  large_mag_usgs['time_diff'].iloc[0] = pd.NA

large_mag_usgs.head()

	Date	mag	Large Event	time_diff
0	1960-01-02	4.04	0	NaN
1	1960-01-05	3.03	0	NaN
2	1960-01-07	3.64	0	NaN
3	1960-01-08	3.10	0	NaN
4	1960-01-11	3.79	0	NaN

Graphing Time Until Large Events

import plotly.express as px
import plotly.graph_objects as go

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fig = go.Figure(data=[go.Bar(
    x=large_mag_etas['Date'],
    y=large_mag_etas['time_diff'],
)])
# Customize the bar appearance
fig.update_traces(marker_line_color='black', marker_line_width=1)  # Set bar color to red and make the bar border black and thicker
# Customize the plot layout
fig.update_layout(
    title='Time Difference Bar Chart (ETAS)',
    xaxis_title='Date',
    yaxis_title='Time Difference (Days)',
)

# Show the plot
fig.show()

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fig = go.Figure(data=[go.Bar(
    x=large_mag_usgs['Date'],
    y=large_mag_usgs['time_diff'],
)])
# Customize the bar appearance
fig.update_traces(marker_color='red', marker_line_color='black', marker_line_width=1)  # Set bar color to red and make the bar border black and thicker

# Customize the plot layout
fig.update_layout(
    title='Time Difference Bar Chart (USGS)',
    xaxis_title='Date',
    yaxis_title='Time Difference (Days)',
)

# Show the plot
fig.show()